Materials Discovery:
Sophisticated computational strategies inspired by artificial intelligence, integrating kinetic experiments, molecular simulations, and machine learning, have the potential for pioneering discovery of the next generation of CO2 capture materials. Precedents with metal-organic frameworks and tertiary amine-based solvents underscore the potential of using a data-driven, computational approach to screen for materials with high selectivity, rapid CO2 uptake and low energy penalty. To push beyond current benchmarks, CORC welcomes computational proposals leveraging AI to uncover materials that optimize absorption rate, capacity, and environmental safety, while also considering scalability and cost for real-world application. This area aims to accelerate the identification and deployment of innovative CO2 capture materials, including organic and inorganic materials and their hybrids, fostering a portfolio of solutions with the potential to significantly reduce atmospheric CO2 and anthropogenic emission.
Digital Twins:
Digital twins (DT) are a means to design and effectively scale CO2 capture and utilization processes based on data from a physical twin where available, and advanced modelling to describe the entire process or process system. Scale up of CCUS (carbon capture, utilisation and storage) technologies is a core ambition of CORC, and therefore we invite proposals that 1) design adaptive and open DT frameworks applicable to CCUS process scale up, and 2) apply advanced real time modelling utilizing AI and/or ML approaches to selected CCUS processes being developed within the CORC activities. A key element is a focus on carbon accounting from a sustainable carbon cycle perspective.
Applicants should emphasise in detail their approach and methodology, as well as the theoretical foundation of the proposed DT framework, but also indicate which mission stream they will support, and how specific DTs can be set up for the focus areas within the mission stream.
